Arrangements for detecting bi-directional artificial intelligence (AI) voice communications and negotiating direct digital communications

ABSTRACT

Arrangements for automatically detecting bi-directional artificial intelligence (AI) communications and automatically negotiating (i.e., switching to alternative) direct digital communications.

RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/277,149, filed Feb. 15, 2019, titled “ARRANGEMENTS FOR DETECTINGBI-DIRECTIONAL ARTIFICIAL INTELLIGENCE (AI) VOICE COMMUNICATIONS ANDNEGOTIATING DIRECT DIGITAL COMMUNICATIONS”. The contents of theaforementioned application are incorporated herein by reference in theirentirety.

TECHNICAL FIELD

Examples described herein are generally related to arrangements forautomatically detecting bi-directional artificial intelligence (AI)communications and automatically negotiating (i.e., switching toalternative) direct digital communications.

BACKGROUND

Users (e.g., private persons; companies; educational institutions;governmental entities; etc.) have been trending towards using AI virtualassistants which use a natural-language interface and are capable of:recognizing a spoken (i.e., voiced) natural-language of a speakingentity: reacting to a content (e.g., command or query) of the spokenlanguage to invoke and perform actions; and returning a result back tothe speaking entity. That is, the AI virtual assistant is able to listenfor and recognize voice statements or queries, perform actions, answerquestions, make recommendations, etc. One example action might includefulfilling requests via interacting with an opposing party across acommunication session (e.g., a voice telephone call). One specificnon-limiting action might be calling a restaurant or hotel to make areservation.

That is, rather than a user (e.g., private person) making a telephonecall himself/herself, the user might instead issue a voice command tohis/her AI virtual assistant, to invoke the AI virtual assistant totelephone a specific restaurant or hotel to make the reservation. The AIvirtual assistant would recognize the voice command, make and establisha telephone connection to the restaurant or hotel, and then utilize itsprovided natural-language interface to voice communicate with theanswering party. The natural-language interface might be configured tosynthesize a human voice which is so highly refined (in tone,inflection, content, accent, etc.) that the answering party might beunable to detect that the calling party talking is an AI virtualassistant, and not a human. Once the reservation is made, the AI virtualassistant communicates the particulars of the reservation back to theuser, e.g., using any of voice, text, email, etc.

As AI virtual assistant usage becomes more-and-more prevalent, there mayoccur an instance where both calling and called ends of a voicecommunication session (e.g., telephone call) are each being conducted byrespective AI virtual assistants. That is, a calling AI virtualassistant and a called AI virtual assistant might be communicatingback-and-forth using voice communications. Disadvantages arise fromback-and-forth voice communications between opposing AI virtualassistants which are digitally-based.

For example, each AI virtual assistant must expend time/resources (e.g.,processing time; processing bandwidth; energy) both to synthesize anoutgoing voice (e.g., via digital-to-analog (D/A) conversion; look-uptable; etc.), and to decipher an incoming voice (e.g., viaanalog-to-digital (A/D) conversion, pattern recognition, etc.). Further,it is inevitable that an incoming voice might occasionally bemis-deciphered or in-decipherable. For example, background sounds,static, noise, etc., might have masked, distorted or clouded a qualityof the incoming voice. In short, there is inefficiency in having twodigitally-based AI virtual assistants communicating back-and-forth witheach other using voice communications.

What is needed are arrangements for automatically detecting instances ofbi-directional artificial intelligence (AI) voice communications, andautomatically invoking a switchover to direct digital communicationsbetween AI entities.

SUMMARY

Provided are arrangements for automatically detecting bi-directionalartificial intelligence (AI) communications and automaticallynegotiating direct digital communications.

That is, example first embodiments are directed to a method ofnegotiating direct digital communication between artificial intelligence(AI) entities, comprising: recognizing, by an AI entity on a first sideof a bi-directional voice communication session, a predeterminedindicator provided from an opposing side of the bi-directional voicecommunication session, indicative of a second AI entity on the opposingside conducting the bi-directional voice communication session;responsive to recognition of the second AI entity, utilizing apredetermined protocol to negotiate connection information for a directdigital communication session between the AI entity and the second AIentity; terminating the bi-directional voice communication sessionbetween the AI entity and the second AI entity after the connectioninformation for the direct digital communication session have beennegotiated; and utilizing the connection information to establish thedirect digital communication session between the AI entity and thesecond AI entity.

In example second embodiments based on the first embodiments, thebi-directional voice communication session includes an in-bandcommunication channel and out-of-band communication channel; thepredetermined indicator is recognized from the in-band communicationchannel.

In example third embodiments based on the second embodiments, thepredetermined indicator recognized from the in-band communicationchannel is at least one item selected from a list of: a predeterminedtone; a predetermined tone which is imperceptible to human hearing; apredetermined audible pattern; a predetermined AI voice; a predeterminedAI salutation; and a predetermined AI phrase.

In example fourth embodiments based on the first embodiments, thebi-directional voice communication session includes an in-bandcommunication channel and out-of-band communication channel; and thepredetermined indicator is recognized from the out-of-band communicationchannel.

In example fifth embodiments based on the fourth embodiments, thepredetermined indicator recognized from the out-of-band communicationchannel is at least one item selected from a list of: a predeterminedcaller identification (ID) number; a predetermined caller ID stringportion; a predetermined caller ID prefix; a predetermined caller IDarea code; a predetermined caller ID text sequence; a predetermined textsequence; and a predetermined header portion.

In example sixth embodiments based on the first embodiments, theconnection information for the direct digital communication session, isat least one of listed items of: a uniform resource locator (URL)useable to establish the direct digital communication session; a tokenuseable to authorize the direct digital communication session; anidentification (ID) token to identify at least one of the AI entity andthe second AI entity; and a protocol to be used in conducting the directdigital communication session.

Example seventh embodiments are directed to a method of negotiatingdirect digital communication between artificial intelligence (AI)entities, comprising: recognizing, by an AI entity on a first side of abi-directional voice communication session, a predetermined indicatorprovided from an opposing side of the bi-directional voice communicationsession, indicative of a second AI entity on the opposing sideconducting the bi-directional voice communication session at acalled-number; terminating the bi-directional voice communicationsession between the AI entity and the second AI entity after recognitionof the second AI entity on the opposing side; responsive to recognitionof the second AI entity, utilizing the called-number to poll apredetermined look-up facility to determine connection information for adirect digital communication session between the AI entity and thesecond AI entity; and utilizing the connection information to establishthe direct digital communication session between the AI entity and thesecond AI entity.

In example eighth embodiments based on the seventh embodiments, thebi-directional voice communication session includes an in-bandcommunication channel and out-of-band communication channel; and thepredetermined indicator is recognized from the in-band communicationchannel.

In example ninth embodiments based on the eighth embodiments, thepredetermined indicator recognized from the in-band communicationchannel is at least one item selected from a list of: a predeterminedtone; a predetermined tone which is imperceptible to human hearing; apredetermined audible pattern; a predetermined AI voice; a predeterminedAI salutation; and a predetermined AI phrase.

In example tenth embodiments based on the seventh embodiments, thebi-directional voice communication session includes an in-bandcommunication channel and out-of-band communication channel; and thepredetermined indicator is recognized from the out-of-band communicationchannel.

In example eleventh embodiments based on the tenth embodiments, thepredetermined indicator recognized from the out-of-band communicationchannel is at least one item selected from a list of: a predeterminedcaller identification (ID) number; a predetermined caller ID stringportion; a predetermined caller ID prefix; a predetermined caller IDarea code; a predetermined caller ID text sequence; a predetermined textsequence; and a predetermined header portion.

In example twelfth embodiments based on the seventh embodiments, theconnection information for the direct digital communication session, isat least one of listed items of: a uniform resource locator (URL)useable to establish the direct digital communication session; a tokenuseable to authorize the direct digital communication session; anidentification (ID) token to identify at least one of the AI entity andthe second AI entity; and a protocol to be used in conducting the directdigital communication session.

In example thirteenth embodiments based on the seventh embodiments, thecalled-number is more particularly a called-telephone-number; and thepredetermined look-up facility utilizes the called-telephone-number anda look-up table (LUT) to determine the connection information for thedirect digital communication session between the AI entity and thesecond AI entity.

In example fourteenth embodiments based on the seventh embodiments, thepredetermined look-up facility is at least one of listed facilities of:a predetermined native look-up facility maintained as a component of theAI entity; a predetermined remote look-up facility maintained remotefrom the AI entity; and a predetermined centralized look-up facilityaccessible via a predetermined URL.

Example fifteenth embodiments are directed to an artificial intelligence(AI) entity comprising: a hardware processor, a non-transitoryprocessor-readable memory embodying code which, when executed by theprocessor, causes the processor to: recognize, on a first side of abi-directional voice communication session, a predetermined indicatorprovided from an opposing side of the bi-directional voice communicationsession, indicative of a second AI entity on the opposing sideconducting the bi-directional voice communication session; responsive torecognition of the second AI entity, utilize a predetermined protocol tonegotiate connection information for a direct digital communicationsession between the AI entity and the second AI entity; terminate thebi-directional voice communication session with the second AI entityafter the connection information for the direct digital communicationsession have been negotiated; and utilize the connection information toestablish the direct digital communication session between the AI entityand the second AI entity.

In example sixteenth embodiments based on the fifteenth embodiments, thebi-directional voice communication session includes an in-bandcommunication channel and out-of-band communication channel; and thepredetermined indicator is recognized from the in-band communicationchannel.

In example seventeenth embodiments based on the sixteenth embodiments,the predetermined indicator recognized from the in-band communicationchannel is at least one item selected from a list of: a predeterminedtone; a predetermined tone which is imperceptible to human hearing; apredetermined audible pattern; a predetermined AI voice; a predeterminedAI salutation; and a predetermined AI phrase.

In example eighteenth embodiments based on the fifteenth embodiments,the bi-directional voice communication session includes an in-bandcommunication channel and out-of-band communication channel; thepredetermined indicator is recognized from the out-of-band communicationchannel.

In example nineteenth embodiments based on the eighteenth embodiments,the predetermined indicator recognized from the out-of-bandcommunication channel is at least one item selected from a list of: apredetermined caller identification (ID) number; a predetermined callerID string portion; a predetermined caller ID prefix; a predeterminedcaller ID area code; a predetermined caller ID text sequence; apredetermined text sequence; and a predetermined header portion.

In example twentieth embodiments based on the fifteenth embodiments, theconnection information for the direct digital communication session, isat least one of listed items of: a uniform resource locator (URL)useable to establish the direct digital communication session; a tokenuseable to authorize the direct digital communication session; anidentification (ID) token to identify at least one of the AI entity andthe second AI entity; and a protocol to be used in conducting the directdigital communication session.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory onlyand are not restrictive of the disclosed embodiments, as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a block diagram example of a communicationarchitecture (e.g., networked system) facilitating two-party (e.g.,caller party and called party) communications.

FIG. 2 illustrates an example flow chart algorithm conducted by at leastone party of the FIG. 1 example architecture.

FIGS. 3A-3C illustrate example plots regarding voice communications.

FIGS. 4A-4C illustrate other example plots regarding voicecommunications.

FIG. 5 illustrates still another example plot regarding voicecommunications.

FIG. 6 illustrates an example flow chart algorithm substitutable for aportion of the FIG. 2 flow chart algorithm.

FIG. 7 illustrates another example flow chart algorithm substitutablefor another portion of the FIG. 2 flow chart algorithm.

FIG. 8 illustrates yet another example flow chart algorithmsubstitutable for yet another portion of the FIG. 2 flow chartalgorithm.

FIG. 9 illustrates still another example flow chart algorithmsubstitutable for still another portion of the FIG. 2 flow chartalgorithm.

FIG. 10 illustrates an example embodiment of a storage medium.

FIG. 11 illustrates an example embodiment of a computing architecturesuch as a server.

DETAILED DESCRIPTION

Reference will now be made in detail to the disclosed embodiments,examples of which are illustrated in the accompanying drawings.

FIG. 1 is a block diagram of communication architecture 100 (e.g.,networked system) which facilitates two-party (e.g., caller or callingparty verses called party) communications. Components in architecture100 may be configured for performing an automatic detecting of instancesof bi-directional artificial intelligence (AI) voice communications, andautomatic invoking of a switchover to direct digital communications,consistent with disclosed embodiments. The embodiments, however, are notlimited to implementation by the communications architecture 100.

As shown in FIG. 1, the communications architecture 100 includes one ormore clients 110, one or more called party servers 150, and one or moreAI support servers 160, and may (in some embodiments) include athird-party hosting server 190. While only one client 110, called partyserver 150, AI support server 160 and third-party hosting server 190 areshown for sake of simplicity and brevity, it will be understood thatarchitecture 100 may include more than one of any of these components.That is, the components and arrangement of the components included inarchitecture 100 may vary. Further, architecture 100 may include othercomponents that perform or assist in the performance of one or moreprocesses consistent with the disclosed embodiments.

In further detail, the client 110, called party server 150, AI supportserver 160 and third-party hosting server 190 are operatively connectedto one another via combinations of paths 120, 140, 170 and 195, andcommunication framework 130. The paths 120, 140, 170 and 195 may be anytype of hardwired, wireless, optical, etc. path(s), or combinationthereof. Further, client 110, called party server 150, AI support server160 and third-party hosting server 190 are illustrated connected to oneor more respective client data storage 114, server data storage 154, AIsupport server data storage 164 and third-party data storage 194 via thepaths 112, 152, 162 and 192, respectively.

While illustrated as being separate from the client 110, called partyserver 150, AI support server 160 and third-party hosting server 190,the client data storage 114, server data storage 154, AI support serverdata storage 164 and third-party storage 194 may instead be at leastpartially embodied within the client 110, called party server 150, AIsupport server 160 and third-party hosting server 190, respectively(whereupon the paths 112, 152, 162 and 192 might be internal connections(e.g., buses, printed circuit board (PCB) trace lines, etc.)). Suchstorages can be employed to store information such as programs,applications, databases, cookies, etc. supportive of performingautomatic detecting of instances of bi-directional artificialintelligence (AI) voice communications, and automatic invoking of aswitchover to direct digital communications, consistent with disclosedembodiments. The paths 112, 152, 162 and 192 may be any type ofhardwired, wireless, optical, etc. path(s), or combination thereof.

Ones of the client 110, client data storage 114, called party server150, server data storage 154, AI support server 160, AI support serverdata storage 164, third-party hosting server 190 and third-party storage194 may exist at differing geographic locations. For example, the client110 and client data storage 114 might be maintained at a residence,business, etc., or on a person of the user 101 (e.g., user's cellphone). The called party server 150 and server data storage 154 might bemaintained at a facility of a company 151 (e.g., restaurant; hotel) orat some other third-party server provider type of facility 191 (e.g.,server farm) per contract (shown representatively by dotted line 199)between the company and a third-party server provider. The AI supportserver 160 and AI support server data storage 164 might be maintained ata facility of an AI support repository, clearinghouse, association,etc., or at a third-party server provider type of facility (e.g., serverfarm) per contract between the AI support association and a third-partyserver provider. Finally, the third-party hosting server 190 andthird-party storage 194 may be maintained at a third-party serverprovider type of facility 191 (e.g., server farm).

The client 110, called party server 150, AI support server 160 andthird-party hosting server 190 may communicate information between eachother via the communication framework 130. The communications framework130 may implement any well-known communications techniques andprotocols. As non-limiting examples, the communications framework 130may be implemented as a packet-switched network (e.g., public networkslike the Internet, private networks like an enterprise intranet, and soforth), a circuit-switched network (e.g., public switched telephonenetwork (PSTN)), or a combination thereof and other networks.

Any network forming part of the communications framework 130 mayimplement various network interfaces arranged to accept, communicate,and connect to another network. A network interface may be regarded as aspecialized form of an input-output interface. Network interfaces mayemploy connection protocols including without limitation: directconnect, Ethernet (e.g., thick, thin, twisted pair 10/100/1900 Base T,and the like), token ring, wireless network interfaces, cellular networkinterfaces, IEEE 802.11a-x network interfaces, IEEE 802.16 networkinterfaces, IEEE 802.20 network interfaces, and the like. Further,multiple network interfaces may be used to engage with variouscommunications network types.

For example, multiple network interfaces may be employed to allow forthe communication over broadcast, multicast, and unicast networks. Stillfurther, communications network may be any one or combination of wiredand/or wireless networks including without limitation: a directinterconnection, a secured custom connection, a private network (e.g.,an enterprise intranet), a public network (e.g., the Internet), aPersonal Area Network (PAN), a Local Area Network (LAN), a MetropolitanArea Network (MAN), an Operating Missions as Nodes on the Internet(OMNI), a Wide Area Network (WAN), a wireless network, a cellularnetwork, and other communications networks.

Alternatively, at least a portion of the communication framework 130 mayin some embodiments, be considered a “cloud”. The term “cloud” is ashorthand reference to cloud computing infrastructure. The cloudincludes one or more communication networks, such as the Internet, forexample, and can further include portions of an industrialcommunications network, such as a local area network (LAN) or a widearea network (WAN). In cloud computing, a computing process may run onone or many connected cloud computers at the same time. In cloudcomputing, the cloud can host and run an application anywhere in theworld. Further, the cloud enables access to the application fromanywhere. Still further, the cloud may include a network or associationof connected computer devices and digital electronic resources that canbe used to perform processing and to relay communications.

Returning to FIG. 1, the client 110 may also be termed as a firstcomputing device associated with a user 101. Although a real-lifepractical system would include many clients and computing devices, onlya single client and computing device is being illustrated and describedin FIG. 1 for the sake of simplicity and brevity. The first computingdevice may be configured to execute, among other programs, an AIapplication 116 and a voice interface VOICE INTERF 118 application.

Any item termed as an “application” within this disclosure is notlimited to existence as a single program and may be embodied via aplurality of (e.g., inter-cooperating) programs. The plurality ofprograms may all be embodied within a single component (e.g., device)provided at a single geographical location, or may be embodied in adisbursed manner in a plurality of components provided in locationsgeographically and/or communicatively distanced from one another.

That is, in some embodiments, the programs of the AI application 116and/or the VOICE INTERF 118 application (as examples) may be embodiedwholly within the client 110 device, the client data storage 114 or acombination of the client 110 device and client data storage 114. Inother embodiments, at least a part of such programs may be embodied inthe cloud via cloud computing, e.g., via a third-party technologycompany providing Internet-related services and products. For simplicityof illustration and description, applications and programs within thisdisclosure will be illustrated and described as being embodied and/orexecuted wholly within a single component (e.g., client 110 device) andat a single location.

The AI application 116 may be configured to operate as an AI virtualassistant which, in one example embodiment, can recognize commands inputby a user, and perform assistive operations for the user responsive torecognized commands. A non-exhaustive list of assistive operations mightinclude: search the Internet; check basic information; schedule events;make telephone calls; schedule alarms; scheduling reminders; adjusthardware settings on the user's device; play music; engage in two-wayconversations. Commands may be input via natural voice, and/or viamanual input (e.g., mouse; keyboard; buttons; switches).

The voice interface VOICE INTERF 118 application may be configured toperform voice synthesis and recognition, and may include a voiceprocessing algorithm where linguistic phenomena such as verbs, phrasesand clauses are recognized. The voice interface VOICE INTERF 118 may beversatile in that it may be configured to process voice inputted to itvia any of plural differing input modes, e.g., microphone, telephone,VoIP, etc.

For the sake of simplicity and brevity, simple examples involving FIG.1's user 101 desiring to secure a dining reservation at a restaurantwill now be used for descriptions leading up to example embodiments ofthe present invention.

More particularly, as one non-AI approach, the user 101 may simplyutilize a telephone 103 to manually place a call himself/herself andestablish a phone call with the restaurant 151, as shown illustrativelyby the FIG. 1 call connection 180 (solid-black-line). The darkenedsquare at the user 101 end of the call connection 180 illustrativelyshows that the call connection 180 is connected to the user 101's phone103, and the darkened square at a restaurant 151 end illustrativelyshows that the call connection 180 is connected to a phone 153 of arestaurant staff person 151 (e.g., reservationist).

In a practical real-world implementation, the illustrated callconnection 180 would actually involve many smaller connections ofdiffering connection types along a connection from the phone 103 to thephone 153, and some of the smaller connections might be moreanalog-friendly (e.g., PSTN) connections as opposed to moredigital-friendly (e.g., Internet) connections.

Of further interest, the call connection 180 may route through theclient 110 of the user 101 and the called party server 150 of therestaurant as shown illustratively via the call connection 180's pathtraversing across the FIG. 1 client 110 and called party server 150blocks. Once the call connection 180 is established, the user 101 andstaff person 151 conduct voice communications until the agreement of areservation date/time is reached. After the reservation is made, the AIvirtual assistant communicates the particulars of the reservation backto the user, e.g., using any of voice, text, email, etc.

Next, a second example will be described where the user 101 uses AI 116to secure the restaurant reservations. More particularly, the user 101may initiate a voice conversation with the AI 116 as shownillustratively via the FIG. 1 connection 182 path (small dash line). Thevoice conversations via the connection path 182 includes handling viathe VOICE INTERF 118 application, as shown illustratively by the callconnection 182's path traversing across the VOICE INTERF 118 applicationblock.

Any VOICE INTERF application mentioned and/or illustrated within thisdisclosure is configured to perform voice synthesis to help producevoice output as dictated by any AI application with which it isassociated, and is configured to help with voice recognition andanalysis regarding content of a voice input (whether received via amicrophone input, telephone line, VoIP, etc.). In effect, the VOICEINTERF application acts as a computer-voice interface, for example,between a human (e.g., the user 101) and the AI application, thusallowing the human and AI application to communicate with each other viavoice.

The user 101 may ask the AI 116 to “Please recommend a restaurant.” TheAI 116 utilizes its built-in intelligence and help from the VOICE INTERF118 application to: recognize the voiced request; invoke a search of theInternet for nearby restaurants; select a proposed restaurant 151according to a predetermined algorithm or criteria; and voice theproposed restaurant 151 to the user 101. Assuming the proposedrestaurant 151 is agreeable, the user 101 may ask the AI 116 to “Make areservation for Friday evening.”

The AI 116 again utilizes its built-in intelligence and help from theVOICE INTERF 118 application to: recognize the voiced request; invoke asearch of the Internet to determine a phone number of the acceptedrestaurant 151; and establish a phone call (shown illustratively usingFIG. 1's call connection 184 (dashed black line)) to the acceptedrestaurant 151. The darkened square at the AI 116 end of the callconnection 184 shows illustratively that the call connection 180 isconnected to (i.e., handled by) the AI 116, and the darkened square at arestaurant 151 end shows illustratively that the call connection 180 isconnected to the phone 153 of the restaurant staff person 151 (e.g.,reservationist).

Again, in a practical real-world implementation, the illustrated callconnection 184 may involve many smaller connections of differingconnection types along a path thereof from the AI 116 to the phone 153,and some of the connections might be more analog-friendly (e.g., PSTN)connections as opposed to more digital-friendly (e.g., Internet)connections.

Further, voice components of the phone call along the call connection184 are handled through the VOICE INTERF 118 application (previouslydescribed), thus allowing the human (restaurant staff member 151) and AIapplication (AI 116) to communicate with each other using voice. Ofnote, if the restaurant staff member 151 does not know in advance thatthe other end of the phone call is being conducted by an AI, and if theVOICE INTERF 118 application's synthesis of a human voice issufficiently refined (in tone, inflection, content, accent, etc.), theanswering party might be unable to detect that the calling party talkingis an AI virtual assistant, and not a human. The user 101 and staffperson 151 conduct voice communications until the agreement of areservation date/time is reached. After the reservation is made, the AI116 virtual assistant communicates the particulars of the reservationback to the user, e.g., using any of voice, text, email, etc.

A third example will be described where the user 101 uses AI 116 tosecure the restaurant reservations, but this time the restaurant is alsousing an AI 156 (instead of the restaurant staff member 151) to handlethe making of reservations. The details of the AI 156 are assumed to bethe same as, or similar to, the details of the AI 116, and accordingly,description of such similar details of the AI 156 are omitted for thesake of brevity and economy. The AI 156, however, may further include areservation-handling application (not shown) and reservation data may bemaintained within a reservations database stored in the server datastorage 154.

A process leading up to the making a phone call (to establish callconnection 186 (long-short dashed line)) between the AI 116 and the AI156 is the same as that described for the above second example involvingconnection 184, except for the following. More particularly, theconnection path 186 is established instead of the call connection 184,and the connection path 186 includes handling via both the VOICE INTERF118 and the VOICE INTERF 158 applications, as shown illustratively bythe call connection 186 path traversing across the VOICE INTERF 118 andthe VOICE INTERF 158 application blocks. Further, the darkened square atthe AI 156 end of the call connection 186 shows illustratively that thecall connection 186 is connected to (i.e., handled by) the AI 156,instead of the restaurant staff person 151 (e.g., reservationist).

Of note, if the AI 116 and AI 156 each do not know in advance that theother end of the phone call is being conducted by an AI, and if theVOICE INTERF applications' synthesis of a human voice is sufficientlyrefined (in tone, inflection, content, accent, etc.), the AI 116 and AI156 conducting the call each might be unable to detect that the opposingparty talking is an AI virtual assistant, and not a human. The AI 116and AI 156 conduct voice communications until the agreement of areservation date/time is reached. After the reservation is made, the AI116 virtual assistant communicates the particulars of the reservationback to the user, e.g., using any of voice, text, email, etc.

As AI virtual assistant usage becomes more-and-more prevalent, there mayoccur an instance such as this, where both calling and called ends of avoice communication session (e.g., telephone call) are each beingconducted by respective AI virtual assistants. That is, a calling AIvirtual assistant and a called AI virtual assistant might becommunicating back-and-forth using voice communications, without everknowing that the opposing party is an AI virtual assistant talking, andnot a human. As mentioned previously, disadvantages arise fromback-and-forth voice communications between opposing AI virtualassistant entities.

For example, each AI virtual assistant must expend time/resources (e.g.,processing time; processing bandwidth; energy) both to synthesize anoutgoing voice (e.g., via digital-to-analog (D/A) conversion; look-uptables; etc.), and to decipher an incoming voice (e.g., viaanalog-to-digital (A/D) conversion; pattern recognition; etc.). Further,it is inevitable that an incoming voice might occasionally bemis-deciphered or in-decipherable. For example, background sounds,static, noise, etc., might have masked, distorted or clouded a qualityof the incoming voice where recognition/analysis is thwarted. In short,there is inefficiency in having two digitally-based AI virtualassistants communicating back-and-forth with each other using analogvoice communications.

Accordingly, what are needed are arrangements for automaticallydetecting instances of bi-directional AI voice communications, andautomatically invoking a switchover to direct digital communicationsrepresenting a more efficient mode of communications for digitally-basedAI entities. FIG. 2's flow chart algorithm 200 includes (in part) anexample algorithm 215 conducted by at least one AI entity of the FIG. 1example architecture, for accomplishing an example AI detection andswitchover.

More particularly, after a Start, a voice communication session (e.g.,telephone call) is established between a caller entity and a calledentity, via an available communication framework path (operation 210).Next, algorithm 215 begins where detection is performed to determinewhether an opposing entity is an AI entity (operation 220). That is, ifthe caller entity is conducting the FIG. 2 algorithm 215, then thecaller AI entity components would perform detection to determine whetherthe called entity is an AI entity. In contrast, if the called AI entityis conducting the FIG. 2 algorithm 215, then called AI entity componentswould perform detection to determine whether the caller entity is an AIentity.

Next, operation 230 would query (based upon the operation 220 detectionresult) whether the opposing entity is an AI entity. If not, an AI-to-AIsituation is not indicated (at least thus far) and a “No” flow branch isfollowed such that a present voice communication session is maintained(operation 240). Operation 242 then queries whether an AI detectionperiod is complete.

More particularly, in some embodiments, an AI detection period may beconfigured to last for a predetermined limited (i.e., finite) amount oftime, e.g., within the first few seconds of establishment of voicecommunications. In other embodiments, an AI detection period may bedefined as lasting for an entirety of a voice communication session. Ifan AI detection period has not completed, a “No” branch is followed toflow back to operation 220 for continued detection of whether theopposing entity is an AI entity. In contrast, if the AI detection periodis complete, a “Yes” branch is followed such that a present voicecommunication session is maintained (operation 244) as an AI-to-AIsituation has not been timely detected by expiration of the AI detectionperiod. Upon completion of the voice communication session, thecommunication session ends.

In further describing operation 230, if operation 230's query finds thatthe opposing entity IS an AI entity (i.e., an AI-to-AI situation isinvolved), then a “Yes” branch is followed and there is a detection ofwhether the present communication session is being conducted via adigital communication path (operation 250). Next, operation 260 wouldquery (based upon the operation 250 detection result) whether thepresent communication session is being conducted via a digitalcommunication path. As an example, a communication path conducted via apacket-switched network or the Internet would be a digital communicationpath. If digital, a “Yes” flow branch is followed such that a digital(not voice) communication session is ultimately conducted via digitalcommunications on a digital communication path without any voiceinterfacing (e.g., VOICE INTERF) functions being conducted (operation280). Upon completion of the communication session, the communicationsession ends.

In contrast, if operation 260's query finds that a non-digitalcommunication path is involved, then a “No” branch is followed whereuponthere is an establishment and use of a digital communication path as areplacement communication path for the prior communication path(operation 270). Then operation 280 (previously described) is performed,and upon completion, the communication session ends.

Next, this disclosure turns to describing some example approaches of howan AI entity might perform FIG. 2 operation 220's detection of whetheran opposing entity is an AI entity. In order to support AI detection, AIentities in general might (in some embodiments) be configured toself-announce themselves within the communications as an AI entity, andto recognize an AI self-announcement of other AI entities when it isreceived.

More particularly, as a first example approach, AI entities might beconfigured (i.e., constructed) in general to include a predeterminedsound signature when outputting voice communications onto any callconnection. For example, FIG. 3A shows an example sound signalarrangement 300A which includes a voice signal 310 and an AI-announcingsound signal 320. While the FIG. 3A example shows three square waves asan example AI-announcing signature, implementation is in no way limitedto such signature, and an infinite number of differently-arranged othertypes or sequencing of signatures may be used.

When such AI-announcing sound signal 320 is provided, then any AI entityconducting the FIG. 2 algorithm 215 might be configured to listen forthe AI-announcing sound signal 320 when receiving voice communications.When the AI-announcing sound signal 320 is detected, then the detectingAI entity would know that the entity with which it is voicecommunicating is another AI entity.

As a further detail, FIG. 3A illustrates the AI-announcing sound signal320 provided separately (i.e., out-of-band) from the voice signal 310.Such may be accomplished, for example, by the call connection being amulti-channeled call connection, and the voice signal 310 andAI-announcing sound signal 320 being provided on differing channels ofthe multi-channeled call connection. Use of a different channel tocommunicate the AI-announcing signature would be advantageous in thatany sound of the AI-announcing signature may be prevented or masked fromany user hearing it. That is, the AI-announcing sound signal from theAI-announcing sound signal channel would be provided only toAI-detecting components tasked with detecting whether an opposing entityis an AI entity, and not to any human listener.

FIG. 3B illustrates another example sound signal arrangement 300B wherethe voice signal 310 and AI-announcing sound signal 320 are providedtogether (i.e., in-band) with each other, e.g., in an overlappingarrangement. Such arrangement may be somewhat disadvantageous in thatany human listener participating in a voice call with an AI-entity as anopposing party would hear the AI-announcing sound signal. At minimum,hearing the AI-announcing sound signal might be an annoyance to thehuman listener. More disadvantageously, recognition by the human of ameaning of the AI-announcing sound signal might result in some usersdisconnecting the call connection in refusal of talking with an AIentity instead of a human. Such might result in a disadvantageous lossof business. For example, if a restaurant utilizes an AI entity fordining reservations, some customers may hang up on their call beforerequesting dining reservations, and may simply call a differentrestaurant which uses a human reservationist.

FIG. 3C illustrates another example situation arrangement 300C where thevoice signal 310 and an AI-announcing sound signal 320′ are providedtogether (i.e., in-band) with each other, but this time the signal 320′is provided in a form where the signal 320 is audibly-imperceptible to ahuman listener (shown representatively by the use of white blocksinstead of black). For example, the signal 320′ may utilize a frequencyor frequencies which are outside of a hearing range of a human listener.Such arrangement is advantageous in that any sound of the AI-announcingsignature may be prevented from any user hearing it. That is, theAI-announcing sound signal from the AI-announcing sound signal channelis audibly-perceptible only to AI-detecting components tasked withdetecting whether an opposing entity is an AI entity, and not to anyhuman listener.

Another example of AI-announcing could involve use of metadata providedas part of a call connection. That is, AI entities in general might beconfigured to self-announce themselves as an AI via inclusion of sometype of self-announcing data within metadata. When such AI-announcingdata is provided within metadata, then any AI entity conducting the FIG.2 algorithm 215 might be configured to check for self-announcing datawithin metadata when receiving voice communications. When theAI-announcing data is detected, then the detecting AI entity would knowthat the entity with which it is voice communicating is another AIentity.

FIG. 4A illustrates example signal plot arrangement 400A where themetadata 420 including AI-announcing data (shown representatively byinclusion of the “(AI)” indicator within the metadata block 420) isprovided separately (i.e., out-of-band) from the voice signal 410. Suchmay be accomplished, for example, by the call connection being amulti-channeled call connection as described previously. Use of adifferent channel to communicate the AI-announcing metadata would beadvantageous in that any sound that might be generated because ofAI-announcing metadata may be prevented or masked from any user hearingit. That is, the AI-announcing metadata from the metadata 420 may beprovided only to AI-detecting components tasked with detecting whetheran opposing entity is an AI entity, and not to any human listener.

FIG. 4B illustrates another example signal plot arrangement 400B wherethe voice signal 410 and the metadata 420 including AI-announcing dataare provided together (i.e., in-band) with each other, e.g., in anoverlapping arrangement. Such arrangement may be somewhatdisadvantageous in that any human listener participating in a voice callwith an AI-entity as an opposing party might hear sound generatedbecause of the metadata 420 being included in-band. At minimum, hearingmetadata-induced sound might be an annoyance to the human listener.

FIG. 4C illustrates another example signal plot arrangement 400C wherethe voice signal 410 and the metadata 420′ including AI-announcing dataare provided together (i.e., in-band) with each other, but this time themetadata 420′ is provided in a form (shown representatively using awhite metadata block instead of black) where either no noise isgenerated responsive to the metadata 420′ or sound generated because ofthe metadata 420′ is audibly-imperceptible to a human listener. Forexample, sound generated because of the metadata 420′ might be afrequency or frequencies which are outside of a hearing range of a humanlistener. Such arrangement is advantageous in that any sound generatedbecause of the metadata 420′ may be prevented from any user hearing it.That is, any sound generated because of the metadata 420′ isaudibly-perceptible only to AI-detecting components tasked withdetecting whether an opposing entity is an AI entity, and not to anyhuman listener.

As to AI-announcing using metadata, in some embodiments, a predeterminedlabel (e.g., “AI-entity” or “AI”) or code (e.g., “99”) may be included(e.g., embedded) within the metadata to disclose that an entity whichoutputted the current communication content is an AI entity. In otherembodiments, a predetermined metadata location (e.g., specific bits orbytes) might be used as a flag to disclose that an entity whichoutputted the current communication content is an AI entity. Forexample, in a single-bit embodiment, a “0” set within the predeterminedAI-designating bit might indicate that the entity is not an AI entity,whereas a “1” might indicate that the entity is an AI entity. On areceiving end, any AI entity conducting the FIG. 2 algorithm 215 mightbe configured to check metadata when receiving voice communications.When positive AI-announcing metadata is detected, then the detecting AIentity would know that the entity with which it is voice communicatingis another AI entity.

Yet another example of AI-announcing could involve use of calleridentification (Caller ID or CID) information provided as part of a callconnection. That is, Caller ID is a telephone service, available inanalog and digital telephone systems (including VoIP), that transmits acaller's telephone number to the called party's telephone equipment whenthe call is being set up. The caller ID service may also include thetransmission of a name associated with the calling telephone number viaa Caller Name CNAM service. Accordingly, in some AI-announcingembodiments, AI entities might be announced by using a telephone numberhaving a predetermined area code (e.g., “999”) which is pre-agreed-towithin the telephone and AI industries as being reserved for AI-entityusage. In other embodiments, a predetermined name such as “AI-Entity”may be transmitted together with a telephone number as part of thecaller name CNAM information. In still other embodiments, both theAI-reserved area code and the “AI-Entity” designating name may be used.

When Caller ID and/or CNAM information is received as part of anincoming call, then any AI entity conducting the FIG. 2 algorithm 215might be configured to check for an AI-reserved area code or an“AI-Entity” designating name. When either is detected, then thedetecting AI entity would know that the entity with which it is voicecommunicating is another AI entity.

Still another example of AI-announcing could involve Voice over InternetProtocol (VoIP; also called voice over IP or IP telephony) technology,where AI-announcing information is included within VoIP packets. Thatis, VoIP is a methodology and a group of technologies for the deliveryof voice communications over Internet Protocol (IP) networks, such asthe Internet, rather than via PSTN. As to packets, a last field in theVoIP packet structure is the payload field which carries the encodedvoice data, whereas preceding fields contain various other information.In some embodiments, a predetermined name such as “AI Entity”, or code(e.g., “99”) or flag (e.g., “0”=non-AI-entity; “1”=AI-entity) may beembedded at a predetermined location of one of the preceding fields, asAI-announcing information.

FIG. 5 illustrates an example VoIP signal arrangement 500 where a stream510 of VoIP data includes a plurality of VoIP packets 520. The notation“(AI)” included within any FIG. 5 packet is used to representativelydesignate that AI-announcing information is included with that packet.Although the FIG. 5 example illustrates “(AI)” information includedwithin every packet, implementation is by no means limited to including“(AI)” information within every packet. More particularly, a fewernumber of packets, or even a single (e.g., leading) packet might containthe “(AI)” information. In some embodiments, the “(AI)” informationmight be included within periodic packets (e.g., every fifth packet).

When VoIP packets are received as part of a voice call, then any AIentity conducting the FIG. 2 algorithm 215 might be configured to checkVoIP packet field information to determine whether “(AI)” information isincluded within any packet, and if yes, whether the “(AI)” informationindicates that the VoIP packets are from an AI entity. If a positiveindication is found, then the detecting AI entity would know that theentity with which it is voice communicating is another AI entity.

As an aside, previously an example was described where FIG. 1's digitalcall connection 186 was assumed to be a digital call connection and waseffectively modified to a shortened call connection 186′ via disablingof the VOICE INTERF 118 and the VOICE INTERF 158 applications. A VoIPcall which involves connection over IP networks, such as the Internet(rather than PSTN), represents a digital call connection. Accordingly,some embodiments may be configured to maintain an original VoIP callconnection and disable operation of the VOICE INTERFs 118, 158applications to handle subsequent packets as containing digital(non-voice) data content rather than VoIP (voice) data content.

Discussion turns next to some embodiments where there may be noAI-announcing information provided by any AI entity. More particularly,in some embodiments, a catalog of known “AI voices” may be compiled andmaintained, for example, by an AI association or clearinghouse, for useas standard AI voices in AI communications. That is, the AI voicecatalog may be stored in, and available (e.g., free of charge; perlicensing; etc.) through a centralized location such as FIG. 1's AISupport Server 160 and Storage 164.

AI entities in general may be configured to access and utilize astandard AI voice selected from the AI voice catalog, for their voiceoutputting. Then, any AI entity conducting the FIG. 2 algorithm 215might be configured to compare a voice heard on a call connection toeach of the known standard AI voices within the AI voice catalog to lookfor a match. Comparison may include comparing voice prints for“signatures”. Further, being “heard” and “a voice” as used in thisdescription is not limited to a voice spectrum which is humanlyperceptible, and instead, being “heard” and “a voice” may more broadlyencompass spectrum which is beyond human perceptibility. For example,being “heard” and “a voice” may more broadly encompass spectrum which ismachine perceptible. When a standard AI voice match is detected, thenthe detecting AI entity would know that the entity with which it isvoice communicating is another AI entity.

In other embodiments, a catalog of predetermined known “AI phrases” iscompiled and maintained, for example, by an AI association orclearinghouse, for use as standard AI phrases in AI communications. Thatis, the AI phrase catalog may be stored in, and available (e.g., free ofcharge; per licensing; etc.) through a centralized location such as FIG.1's AI Support Server 160 and Storage 164. One example salutary phrasemight be “Greetings humanoid.” AI entities in general may be configuredto access and utilize standard AI phrases selected from the AI phrasecatalog. Then, any AI entity conducting the FIG. 2 algorithm 215 mightbe configured to compare phrases heard on a call connection to each ofthe standard AI phrases within the AI phrase catalog to look for amatch. When a standard AI phrase match is detected, then the detectingAI entity would know that the entity with which it is voicecommunicating is another AI entity.

Turning now to more detailed descriptions of portions of the FIG. 2 flowalgorithm, FIG. 6 illustrates a more detailed example flow chartalgorithm 600 (including operations 220′) substitutable for, and servingas a basis for further description of, operation 220. In order todetermine whether an opposing entity is an AI entity, a Start isfollowed by an operation 610 obtaining information which includes AIindicator data indicative of whether an opposing entity is an AI entity.Again, as non-limiting examples, such information might be obtained fromone or more of: signature sound signals, metadata content, Caller ID(CID) content, Caller Name (CNAM) content, VOIP content, catalogedAI-voice-containing sound signals, cataloged AI-phrase-containing soundsignals, etc., as mentioned above during the FIGS. 3A-3C, 4A-4C and 5discussions.

Thereafter, operation 620 is performed to extract AI indicator data fromthe obtained information, indicative of whether an opposing entity is anAI entity. In line with the above information examples, non-exhaustiveAI indicator data might include: possible signature-containing-portionsof sound signals, predetermined metadata portions, predetermined CallerID (CID) portions, predetermined Caller Name (CNAM) portions,predetermined VOIP portions, sampled possible-AI-voice-containing soundportions, sampled possible-AI-phrase-containing sound portions, etc.

Finally, operation 630 is performed to analyze the extracted AIindicator data to determine whether extracted artificial intelligenceindicator data meets any predetermined AI indicators which areindicative of an AI entity, thereby to obtain an AI entity determinationresult. Analyzing the extracted AI indicator data may be conducted viaone or more of the analyses described above including with respect toFIGS. 3A-3C, 4A-4C and 5. Once a result is obtained, the algorithm 220′is ended, and continued operation passes to FIG. 2's operation 230 whichutilizes the result in its query.

Next, FIG. 7 illustrates a more detailed example flow chart algorithm700 (including operations 250′) substitutable for, and serving as abasis for further description of, operation 250 of the FIG. 2 flow chartalgorithm. In order to perform detection of whether the presentcommunication session is being conducted via a digital communicationpath, a Start is followed by an operation 710 obtaining informationwhich includes communication path data indicating a type of a currentcommunication path. For example such information might be: propertyinformation associated with a communication path of the communicationsession; a protocol followed in conducting the communication sessionalong the present communication path; etc.

Operation 720 then extracts the communication path data from theobtained information, with the communication path data being indicativeof a type of current communication path. Non-exhaustive communicationpath data might be: a “packet-switched” setting; an “IPv4” setting; an“IPv6” setting; a “PSTN” setting; etc.

Finally, operation 730 analyzes the extracted communication path data todetermine whether the current communication path is a known type ofdigital communication path, thereby resulting in a digital communicationpath determination result. For example, analysis of a “packet-switched”setting, “IPv4” setting or “IPv6” setting would obtain a resultindicative that a current communication path is a digital communicationpath. In contrast, analysis of a “PSTN” setting would obtain a resultindicative that a current communication path is NOT a digitalcommunication path. Once a result is obtained, the algorithm 250′ isended, and continued operation passes to FIG. 2's operation 260 whichutilizes the result in its query.

Description turns next to approaches for establishing a new digitalcommunication path as a replacement communication path, as called for inFIG. 2's operations 270. More particularly, in some embodiments, the AI116 and AI 156 applications (as AI entities) may continue to voicecommunicate with each other for a while after detection is made of anopposing AI entity on the other end of the call, in order to exchangeaddressing information to describe an address useable to connect to setup a new digital communication path to further communicate. Exampleaddressing information might be an IPv4 address, IPv6 address, or adomain name address.

Given that IPv4 and IPv6 addresses are numerical (e.g., 172.16.254.1),one of the AI entities (e.g., the called restaurant AI entity) mightsimply voice the numbers of its IP address, such as “One seven two dotone six dot two five four dot one.” Alternatively, given that a domainname address is formed of alphanumeric characters (e.g.,www.restaurantX.com/reservations), the AI entity might simply voice thealphanumeric characters of its domain name address, such as “www.restaur. . . ”.

Responsive to exchange of addressing information, the AI entityreceiving the addressing information (e.g., the calling AI entityseeking a dining reservation) would then utilize such addressinginformation to establish a digital communication path (such as shown byFIG. 1's communication path 188 and continue with direct digitalcommunications between the two AI entities. Alternatively, theaddressing information might connect the calling AI entity 116 to athird-party AI entity 196 of a third-party (e.g., dining reservation)hosting site maintained at a third-party server provider type offacility 191 (e.g., server farm). Such may be illustrated by the FIG. 1communication path 189 (double-short-dash-single-dot line). The originalvoice communication path (e.g., FIG. 1's communication path 186) may bedisconnected when: addressing information exchange is successful; whenthe digital communication path is established; etc.

In addition to exchanging addressing information, other types ofinformation might also be exchanged during the voice communications. Insome embodiments, a token may be exchanged, where the token might beuseable for: identifying either or both of the AI entities;authentication in establishing the digital communication path;identifying the original voice communication session; a format orprotocol which should be used for the direct digital communications; anature of the original voice communication query (e.g., reservationrequest); business hours for contacting the AI entity; etc.Implementation of embodiments of the invention is by no means limited tothe above-mentioned types of exchanged information.

In some other embodiments, there may be no exchange of addressing orother information between the two AI entities in the voice callconnection once it is determined that an AI-entity-to-AI-entity callsituation exists. More particularly, in some embodiments the calling AIentity (for example) might be configured to simply disconnect the voicecommunication session, and then use an alternative approach to obtaininformation which is helpful to establish a digital communicationsession to finish communications digitally.

More particularly, in some embodiments, the calling AI entity (forexample) might be configured to search the webpage of the called (e.g.,restaurant) entity to obtain an IP address or domain name address (andother information such as described above) for establishing a digitalcommunication session to perform direct digital communications with thecalled entity. Thereafter, the calling AI entity may use the obtained IPaddress and other information to establish digital communication sessionand finish the communications with direct digital communications.

In other embodiments, an AI association or clearing house might solicitand compile a database of contact information (including direct digitalcommunication path instructions) for large numbers of AI entities, andmake that available for reference. That is, the database may provide across-reference, for example of: AI entity name; AI entity telephonenumber; AI entity IP address or domain name; AI entity direct digitalcommunication path address; AI entity communication format and/orprotocol; etc. Implementation of embodiments of the invention is by nomeans limited to the above-mentioned types of exchanged information.

The calling AI entity (for example) may then be configured to: use apredetermined Internet address to contact the database-hosting site(e.g., the FIG. 1 AI support server 160); and then utilize the knowninformation of the called (e.g., restaurant) entity (such as the AIentity name; AI entity telephone number; etc.) to poll the database andobtain cross-reference information such as the AI entity direct digitalcommunication path address, AI entity communication format and/orprotocol, etc.; and then utilize the obtained information to establish adigital communication session and finish the communications with thecalled (restaurant) AI entity via direct digital communications.

In still other embodiments, the called (restaurant) AI entity may beconfigured to text IP address or domain name address (and otherinformation such as described above) for establishing a digitalcommunication session back to the telephone number of the calling AIentity, with the telephone number being gleaned from metadata, CallerID, etc. of an incoming call. Thereafter, the calling AI entity may usethe obtained IP address and other information from the text to establishdigital communication session and finish the communications with directdigital communications.

In line with the above descriptions, FIG. 8 illustrates a more detailedexample flow chart algorithm 800 (including operations 270′)substitutable for, and serving as a basis for further description of,operation 270 of the FIG. 2 flow chart algorithm. That is, in order toestablish a digital communication path as a replacement communicationpath for the prior communication path, a Start is followed by anoperation 810 obtaining information which includes digital connectionsetup data providing at least a connectable address for setting up adigital connection for direct digital communications. Again, asnon-limiting examples, such information might be obtained from one ormore of: voice-communicated information; webpage-obtained information;database-obtained information; text-message-obtained information; etc.,as mentioned above. Implementation of embodiments of the invention is byno means limited to the above-mentioned information.

Thereafter, operation 820 is performed to extract digital connectionsetup data from the obtained information, with the extracted data beinguseable for (e.g., guiding) setting up of a digital connection fordirect digital communications. Again, non-exhaustive digital connectionsetup data might include: an IPv4 address; an IPv6 address; a domainname address; a token that might be useable for: identifying either orboth of the AI entities; authentication in establishing the digitalcommunication path; identifying the original voice communicationsession; a format or protocol which should be used for the directdigital communications; business hours for contacting the AI entity;etc.

Finally, operation 830 is performed to use the extracted digitalconnection setup data to establish a digital connection as a replacementconnection. As an example, a caller AI entity may establish a digitalconnection by utilizing an extracted IPv6 address and extractedprotocol. Once a digital connection is established, the algorithm 270′is ended, and continued operation passes to FIG. 2's operation 280 forconducting the digital communication session.

Description turns momentarily to FIG. 1 to describe a switching-overregarding two digital communication connections or paths. Moreparticularly, in considering the third example's call connection 186, ifthe call connection 186 is determined as a digital (e.g., apacket-switched or Internet) communication path via FIG. 2's operation260, then the “Yes” flow branch is followed to operation 280, but inthis case, the call connection 186 is still being used inefficiently inthat it includes handling via both the VOICE INTERF 118 and the VOICEINTERF 158 applications.

In order to effect operation 280 so that a digital (not voice)communication session is thereafter conducted via the digitalcommunication path without any voice interfacing (e.g., VOICE INTERF)functions being conducted, steps may be taken to remove or disable boththe VOICE INTERF 118 and the VOICE INTERF 158 applications from furtherhandling content of a continuing communication session. Of course, toremove or disable both the VOICE INTERF 118 and the VOICE INTERF 158applications from opposing ends of the call connection, there may besome type of coordination or hand-shaking performed between the two AIentities to synchronize regarding removal or disabling, and thencommunicating without voice. Otherwise communications between the two AIentities might experience failure at least temporarily until their modesof operation are synchronized.

FIG. 9 illustrates a more detailed example flow chart algorithm 900(including operations 280′) substitutable for, and serving as a basisfor further description of, operation 280 of the FIG. 2 flow chartalgorithm. In order to conduct a digital (not voice) communicationsession thereafter via the digital communication path without any voiceinterfacing (e.g., VOICE INTERF) functions being conducted, a Start isfollowed by an operation 910 asking a query of whether a voice interface(e.g., VOICE INTERF) is currently set operational to synthesize anoutput voice and to process a voice input of a communication. If notpresently operational (i.e., the voice interfacing is not handlingcommunications), a “No” branch is followed to conduct the digitalcommunications to complete a communication session (operation 930). Uponcompletion of the voice communication session, the communication sessionends.

In contrast, if operation 910's query determines that the voiceinterfacing is presently operational (i.e., the voice interfacing IShandling communications), a “Yes” branch is followed where a disablingof operation of the voice interface (e.g., VOICE INTERF) for allowingdigital communications without voice interface processing, is effected.Thereafter, operation 930 (previously described) is conducted.

Disabling (or removing) may be accomplished in numerous differing ways.For example: turning the applications off; routing communication contentthrough a differing route or processing sequence to avoid suchapplications; setting programming flags or switches so that suchapplications are disabled; etc. Such disabling or removing may, in someembodiments, be able to be accomplished without disconnection of thecall connection 186.

As a result, the call connection 186 is still operable but has beeneffectively modified as shown illustratively by FIG. 1's changed,shortened, transformed, etc., call connection 186′ (lighter-shadedlong-dashed-short-dashed line). Thereafter, more efficient directdigital communications are conducted between the AI 116 and AI 156applications along the call connection 186′, without the inefficienciescaused via handling with the VOICE INTERF 118 and VOICE INTERF 158applications (FIG. 2 operation 280).

As a second digital communication connection or path example, if thepresent call connection (e.g., FIG. 1's call connection 186) isdetermined (e.g., via FIG. 2's operation 260) not to be a digitalcommunication path (e.g., is a PSTN connection) and the “No” flow branchis followed, some embodiments may establish a wholly new digitalcommunication connection to replace the non-digital call connection 186.FIG. 1 shows a new digital communication connection 188(short-dash-double-dot-line) allowing direct digital communicationsbetween the AI 116 and AI 156 applications without involving the VOICEINTERF 118 and VOICE INTERF 158 applications. Given that communicationswill be conducted via the new digital communication connection, theprior voice call connection may be released (e.g., disconnected) to freeup system resources.

Next, FIG. 10 illustrates an embodiment of a storage medium 1000.Storage medium 1000 may include any non-transitory computer-readablestorage medium or machine-readable storage medium, such as an optical,magnetic or semiconductor storage medium. In various embodiments,storage medium 1000 may include an article of manufacture. In someembodiments, storage medium 1000 may store computer-executableinstructions, such as computer-executable instructions to implement oneor more of logic flows or operations described herein, such as withrespect to flow algorithms 200 of FIG. 2, 600 of FIG. 6, 700 of FIG. 7,800 of FIG. 8 and 900 of FIG. 9. Storage medium 1000 may also storefurther computer-executable instructions, such as computer-executableinstructions to implement other logic flows or operations, and may storedata.

Examples of a computer-readable storage medium or machine-readablestorage medium may include any tangible media capable of storingelectronic data, including volatile memory or non-volatile memory,removable or non-removable memory, erasable or non-erasable memory,writeable or re-writeable memory, and so forth. Examples ofcomputer-executable instructions may include any suitable type of code,such as source code, compiled code, interpreted code, executable code,static code, dynamic code, object-oriented code, visual code, and thelike. The embodiments are not limited in this context.

FIG. 11 illustrates an embodiment of an exemplary computing architecture1100 that may be suitable for implementing any of the components (e.g.,computers, servers, client devices, etc.) of various embodiments asdescribed anywhere within this disclosure. In various embodiments, thecomputing architecture 1100 may include or be implemented as part of anelectronic device. In some embodiments, the computing architecture 1100may be representative, for example, of a processor server thatimplements one or more components of the FIG. 1 system, such as theabove-described components. In some embodiments, computing architecture1100 may be representative, for example, of a computing device thatimplements one or more components of the client 110, called party server150, AI support server 160 and third-party hosting server 190arrangements. The embodiments are not limited in this context.

As used in this application, the terms “system” and “component” and“module” are intended to refer to a computer-related entity, eitherhardware, a combination of hardware and software, software, or softwarein execution, examples of which are provided by the exemplary computingarchitecture 1100. For example, a component can be, but is not limitedto being, a process running on a processor, a processor, a hard diskdrive, multiple storage drives (of optical and/or magnetic storagemedium), an object, an executable, a thread of execution, a program,and/or a computer. By way of illustration, both an application runningon a server and the server can be a component. One or more componentscan reside within a process and/or thread of execution, and a componentcan be localized on one computer and/or distributed between two or morecomputers. Further, components may be communicatively coupled to eachother by various types of communications media to coordinate operations.The coordination may involve the uni-directional or bi-directionalexchange of information. For instance, the components may communicateinformation in the form of signals communicated over the communicationsmedia. The information can be implemented as signals allocated tovarious signal lines. In such allocations, each message is a signal.Further embodiments, however, may alternatively employ data messages.Such data messages may be sent across various connections. Exemplaryconnections include parallel interfaces, serial interfaces, and businterfaces.

The computing architecture 1100 includes various common computingelements, such as one or more processors, multi-core processors,co-processors, memory units, chipsets, controllers, peripherals,interfaces, oscillators, timing devices, video cards, audio cards,multimedia input/output (I/O) components, power supplies, and so forth.The embodiments, however, are not limited to implementation by thecomputing architecture 1100.

As shown in FIG. 11, the computing architecture 1100 includes aprocessing unit 1104, a system memory 1106 and a system bus 1108. Theprocessing unit 1104 can be any of various commercially availableprocessors, including without limitation an AMD® Athlon®, Duron® andOpteron® processors; ARM® application, embedded and secure processors;IBM® and Motorola® DragonBall® and PowerPC® processors; IBM and Sony®Cell processors; Intel® Celeron®, Core (2) Duo®, Itanium®, Pentium®,Xeon®, and XScale® processors; and similar processors. Dualmicroprocessors, multi-core processors, and other multi-processorarchitectures may also be employed as the processing unit 1104.

The system bus 1108 provides an interface for system componentsincluding, but not limited to, the system memory 1106 to the processingunit 1104. The system bus 1108 can be any of several types of busstructure that may further interconnect to a memory bus (with or withouta memory controller), a peripheral bus, and a local bus using any of avariety of commercially available bus architectures. Interface adaptersmay connect to the system bus 1108 via a slot architecture. Example slotarchitectures may include without limitation Accelerated Graphics Port(AGP), Card Bus, (Extended) Industry Standard Architecture ((E)ISA),Micro Channel Architecture (MCA), NuBus, Peripheral ComponentInterconnect (Extended) (PCI(X)), PCI Express, Personal Computer MemoryCard International Association (PCMCIA), and the like.

The system memory 1106 may include various types of computer-readablestorage media in the form of one or more higher speed memory units, suchas read-only memory (ROM), random-access memory (RAM), dynamic RAM(DRAM), Double-Data-Rate DRAM (DDRAM), synchronous DRAM (SDRAM), staticRAM (SRAM), programmable ROM (PROM), erasable programmable ROM (EPROM),electrically erasable programmable ROM (EEPROM), flash memory (e.g., oneor more flash arrays), polymer memory such as ferroelectric polymermemory, ovonic memory, phase change or ferroelectric memory,silicon-oxide-nitride-oxide-silicon (SONOS) memory, magnetic or opticalcards, an array of devices such as Redundant Array of Independent Disks(RAID) drives, solid state memory devices (e.g., USB memory, solid statedrives (SSD) and any other type of storage media suitable for storinginformation. In the illustrated embodiment shown in FIG. 11, the systemmemory 1106 can include non-volatile memory 1110 and/or volatile memory1112. A basic input/output system (BIOS) can be stored in thenon-volatile memory 1110.

The computer 1102 may include various types of computer-readable storagemedia in the form of one or more lower speed memory units, including aninternal (or external) hard disk drive (HDD) 1114, a magnetic floppydisk drive (FDD) 1116 to read from or write to a removable magnetic disk1118, and an optical disk drive 1120 to read from or write to aremovable optical disk 1122 (e.g., a CD-ROM or DVD). The HDD 1114, FDD1116 and optical disk drive 1120 can be connected to the system bus 1108by a HDD interface 1124, an FDD interface 1126 and an optical driveinterface 1128, respectively. The HDD interface 1124 for external driveimplementations can include at least one or both of Universal Serial Bus(USB) and IEEE 1594 interface technologies.

The drives and associated computer-readable media provide volatileand/or nonvolatile storage of data, data structures, computer-executableinstructions, and so forth. For example, a number of program modules canbe stored in the drives and memory units 1110, 1112, including anoperating system 1130, one or more application programs 1132, otherprogram modules 1134, and program data 1136. In one embodiment, the oneor more application programs 1132, other program modules 1134, andprogram data 1136 can include, for example, the various applicationsand/or components of the aforementioned servers of the presentdisclosure.

A user can enter commands and information into the computer 1102 throughone or more wire/wireless input devices, for example, a keyboard 1138and a pointing device, such as a mouse 1140. Other input devices mayinclude microphones, infra-red (IR) remote controls, radio-frequency(RF) remote controls, game pads, stylus pens, card readers, dongles,finger print readers, gloves, graphics tablets, joysticks, keyboards,retina readers, touch screens (e.g., capacitive, resistive, etc.),trackballs, trackpads, sensors, styluses, and the like. These and otherinput devices are often connected to the processing unit 1104 through aninput device interface 1142 that is coupled to the system bus 1108, butcan be connected by other interfaces such as a parallel port, IEEE 994serial port, a game port, a USB port, an IR interface, and so forth.

A monitor 1144 or other type of display device is also connected to thesystem bus 1108 via an interface, such as a video adaptor 1146. Themonitor 1144 may be internal or external to the computer 1102. Inaddition to the monitor 1144, a computer typically includes otherperipheral output devices, such as speakers, printers, and so forth.

The computer 1102 may operate in a networked environment using logicalconnections via wire and/or wireless communications to one or moreremote computers, such as a remote computer 1148. The remote computer1148 can be a workstation, a server computer, a router, a personalcomputer, portable computer, microprocessor-based entertainmentappliance, a peer device or other common network node, and typicallyincludes many or all of the elements described relative to the computer1102, although, for purposes of brevity, only a memory/storage device1150 is illustrated. The logical connections depicted includewire/wireless connectivity to a local area network (LAN) 1152 and/orlarger networks, for example, a wide area network (WAN) 1154. Such LANand WAN networking environments are commonplace in offices andcompanies, and facilitate enterprise-wide computer networks, such asintranets, all of which may connect to a global communications network,for example, the Internet.

When used in a LAN networking environment, the computer 1102 isconnected to the LAN 1152 through a wire and/or wireless communicationnetwork interface or adaptor 1156. The adaptor 1156 can facilitate wireand/or wireless communications to the LAN 1152, which may also include awireless access point disposed thereon for communicating with thewireless functionality of the adaptor 1156.

When used in a WAN networking environment, the computer 1102 can includea modem 1158, or is connected to a communications server on the WAN1154, or has other means for establishing communications over the WAN1154, such as by way of the Internet. The modem 1158, which can beinternal or external and a wire and/or wireless device, connects to thesystem bus 1108 via the input device interface 1142. In a networkedenvironment, program modules depicted relative to the computer 1102, orportions thereof, can be stored in the remote memory/storage device1150. It will be appreciated that the network connections shown areexemplary and other means of establishing a communications link betweenthe computers can be used.

The computer 1102 is operable to communicate with wire and wirelessdevices or entities using the IEEE 802 family of standards, such aswireless devices operatively disposed in wireless communication (e.g.,IEEE 802.16 over-the-air modulation techniques). This includes at leastWi-Fi (or Wireless Fidelity), WiMax, and Bluetooth™ wirelesstechnologies, among others. Thus, the communication can be a predefinedstructure as with a conventional network or simply an ad hoccommunication between at least two devices. Wi-Fi networks use radiotechnologies called IEEE 802.11x (a, b, g, n, etc.) to provide secure,reliable, fast wireless connectivity. A Wi-Fi network can be used toconnect computers to each other, to the Internet, and to wire networks(which use IEEE 802.3-related media and functions).

Various embodiments may be implemented using hardware elements, softwareelements, or a combination of both. Examples of hardware elements mayinclude processors, microprocessors, circuits, circuit elements (e.g.,transistors, resistors, capacitors, inductors, and so forth), integratedcircuits, application specific integrated circuits (ASIC), programmablelogic devices (PLD), digital signal processors (DSP), field programmablegate array (FPGA), logic gates, registers, semiconductor device, chips,microchips, chip sets, and so forth. Examples of software may includesoftware components, programs, applications, computer programs,application programs, system programs, machine programs, operatingsystem software, middleware, firmware, software modules, routines,subroutines, functions, methods, procedures, software interfaces,application program interfaces (API), instruction sets, computing code,computer code, code segments, computer code segments, words, values,symbols, or any combination thereof. Determining whether an embodimentis implemented using hardware elements and/or software elements may varyin accordance with any number of factors, such as desired computationalrate, power levels, heat tolerances, processing cycle budget, input datarates, output data rates, memory resources, data bus speeds and otherdesign or performance constraints.

One or more aspects of at least one embodiment may be implemented byrepresentative instructions stored on a machine-readable medium whichrepresents various logic within the processor, which when read by amachine causes the machine to fabricate logic to perform the techniquesdescribed herein. Such representations, known as “IP cores” may bestored on a tangible, machine readable medium and supplied to variouscustomers or manufacturing facilities to load into the fabricationmachines that actually make the logic or processor. Some embodiments maybe implemented, for example, using a machine-readable medium or articlewhich may store an instruction or a set of instructions that, ifexecuted by a machine, may cause the machine to perform a method and/oroperations in accordance with the embodiments. Such a machine mayinclude, for example, any suitable processing platform, computingplatform, computing device, processing device, computing system,processing system, computer, processor, or the like, and may beimplemented using any suitable combination of hardware and/or software.The machine-readable medium or article may include, for example, anysuitable type of memory unit, memory device, memory article, memorymedium, storage device, storage article, storage medium and/or storageunit, for example, memory, removable or non-removable media, erasable ornon-erasable media, writeable or re-writeable media, digital or analogmedia, hard disk, floppy disk, Compact Disk Read Only Memory (CD-ROM),Compact Disk Recordable (CD-R), Compact Disk Rewriteable (CD-RW),optical disk, magnetic media, magneto-optical media, removable memorycards or disks, various types of Digital Versatile Disk (DVD), a tape, acassette, or the like. The instructions may include any suitable type ofcode, such as source code, compiled code, interpreted code, executablecode, static code, dynamic code, encrypted code, and the like,implemented using any suitable high-level, low-level, object-oriented,visual, compiled and/or interpreted programming language.

While, for purposes of simplicity of explanation, the one or moremethodologies shown herein are shown and described as a series of acts,those skilled in the art will understand and appreciate that themethodologies are not limited by the order of acts. Some acts may, inaccordance therewith, occur in a different order and/or concurrentlywith other acts from that shown and described herein. For example, thoseskilled in the art will understand and appreciate that a methodologycould alternatively be represented as a series of interrelated states orevents, such as in a state diagram. Moreover, not all acts illustratedin a methodology may be required for a novel implementation.

A logic flow may be implemented in software, firmware, and/or hardware.In software and firmware embodiments, a logic flow may be implemented bycomputer executable instructions stored on at least one non-transitorycomputer readable medium or machine readable medium, such as an optical,magnetic or semiconductor storage. The embodiments are not limited inthis context.

It should be appreciated that the example embodiments shown in the blockdiagram of several FIGS. may represent one functionally descriptiveexample of many potential implementations. Accordingly, division,omission or inclusion of block functions depicted in the accompanyingfigures does not infer that the hardware components, circuits, softwareand/or elements for implementing these functions would be necessarily bedivided, omitted, or included in embodiments. Some examples may bedescribed using the expression “in one example” or “an example” alongwith their derivatives. These terms mean that a particular feature,structure, or characteristic described in connection with the example isincluded in at least one example. The appearances of the phrase “in oneexample” in various places in the specification are not necessarily allreferring to the same example.

In the context of the present disclosure, unless expressly providedotherwise, the words “first”, “second”, “third”, etc. have been used asadjectives only for the purpose of allowing for distinction between thenouns that they modify from one another, and not for the purpose ofdescribing any particular relationship between those nouns.

As may or may not have been mentioned previously, various features,operations, etc. of this invention may be practiced “simultaneously”,“concurrently” or “parallelly”. As used within a content of thisinvention, the term ‘simultaneous’ means that two things (e.g.,collecting; analyzing, etc., of differing information) happen at thesame time (i.e., at least partially occur or overlap in time), while theterm ‘concurrent’ means that the two things may occur during the sameperiod of time, but do not necessarily happen at the same time.Concurrent is the broader term, and may include instances of thingsoccurring simultaneously. If two things (e.g., collecting; analyzing,etc., of differing information) overlap in time partially but notcompletely, the things may be described as occurring concurrently, whilethe overlapping portion may be described as occurring simultaneously.Further, the term “parallel” means that two things occur along twodiffering paths or via differing operations. Parallel may includeinstances which occur simultaneously, instances which occurconcurrently, and/or instances occurring at wholly differing timeperiods.

In this disclosure, the term “real time” refers to a time scale that issubstantially simultaneous to an item or instance which provoked asubsequent action. In contrast, the term “near real time” refers to atime scale that is slower than the time scale referred to as “realtime,” for example by about one or more orders of magnitude, or byrequiring a finite amount of time (e.g., milliseconds) rather than beingsubstantially simultaneous.

The foregoing description of example embodiments has been presented forthe purposes of illustration and description. It is not intended to beexhaustive or to limit the present disclosure to the precise formsdisclosed. Many modifications and variations are possible in light ofthis disclosure. It is intended that the scope of the present disclosurebe limited not by this detailed description, but rather by the claimsappended hereto. Future filed applications claiming priority to thisapplication may claim the disclosed subject matter in a differentmanner, and may generally include any set of one or more limitations asvariously disclosed or otherwise demonstrated herein.

The invention claimed is:
 1. A method of negotiating direct digitalcommunication between artificial intelligence (AI) entities, comprising:recognizing, by an AI entity on a first side of a bi-directional voicecommunication session, a predetermined indicator provided from anopposing side of the bi-directional voice communication session, thepredetermined indicator indicative of a second AI entity on the opposingside conducting the bi-directional voice communication session at acalled-number; terminating the bi-directional voice communicationsession between the AI entity and the second AI entity after recognitionof the second AI entity on the opposing side; responsive to recognitionof the second AI entity, utilizing the called-number to poll apredetermined look-up facility to determine connection information for adirect digital communication session between the AI entity and thesecond AI entity; utilizing the connection information to establish thedirect digital communication session between the AI entity and thesecond AI entity; and utilizing the direct digital communication sessionto communicate non-voice digital data without voice interfacefunctionality.
 2. The method as claimed in claim 1, wherein: thebi-directional voice communication session includes an in-bandcommunication channel and out-of-band communication channel; and thepredetermined indicator is recognized from the in-band communicationchannel.
 3. The method as claimed in claim 2, wherein: the predeterminedindicator recognized from the in-band communication channel is at leastone item selected from a list of: a predetermined tone; a predeterminedtone which is imperceptible to human hearing; a predetermined audiblepattern; a predetermined AI voice; a predetermined AI salutation; and apredetermined AI phrase.
 4. The method as claimed in claim 1, wherein:the bi-directional voice communication session includes an in-bandcommunication channel and out-of-band communication channel; and thepredetermined indicator is recognized from the out-of-band communicationchannel.
 5. The method as claimed in claim 4, wherein: the predeterminedindicator recognized from the out-of-band communication channel is atleast one item selected from a list of: a predetermined calleridentification (ID) number; a predetermined caller ID string portion; apredetermined caller ID prefix; a predetermined caller ID area code; apredetermined caller ID text sequence; a predetermined text sequence;and a predetermined header portion.
 6. The method as claimed in claim 1,wherein: the connection information for the direct digital communicationsession, is at least one of listed items of: a uniform resource locator(URL) useable to establish the direct digital communication session; atoken useable to authorize the direct digital communication session; anidentification (ID) token to identify at least one of the AI entity andthe second AI entity; and a protocol to be used in conducting the directdigital communication session.
 7. The method as claimed in claim 1,wherein: the called-number is a called-telephone-number; and thepredetermined look-up facility utilizes the called-telephone-number anda look-up table (LUT) to determine the connection information for thedirect digital communication session between the AI entity and thesecond AI entity.
 8. The method as claimed in claim 1, wherein: thepredetermined look-up facility is at least one of listed facilities of:a predetermined native look-up facility maintained as a component of theAI entity; a predetermined remote look-up facility maintained remotefrom the AI entity; and a predetermined centralized look-up facilityaccessible via a predetermined URL.
 9. A non-transitory machine-readablemedium containing instructions, which when executed by a processor,cause the processor to perform operations, the operations to: recognize,by an artificial intelligence (AI) entity on a first side of abi-directional voice communication session, a predetermined indicatorprovided from an opposing side of the bidirectional voice communicationsession, the predetermined indicator indicative of a second AI entity onthe opposing side conducting the bi-directional voice communicationsession at a called-number; terminate the bi-directional voicecommunication session between the AI entity and the second AI entityafter recognition of the second AI entity on the opposing side;responsive to recognition of the second AI entity, utilize thecalled-number to poll a predetermined look-up facility to determineconnection information for a direct digital communication sessionbetween the AI entity and the second AI entity; utilize the connectioninformation to establish the direct digital communication sessionbetween the AI entity and the second AI entity; and utilize the directdigital communication session to communicate non-voice digital datawithout voice interface functionality.
 10. The machine-readable mediumof claim 9, wherein: the bi-directional voice communication sessionincludes an in-band communication channel and out-of-band communicationchannel; and the predetermined indicator is recognized from the in-bandcommunication channel.
 11. The machine-readable medium of claim 10,wherein: the predetermined indicator recognized from the in-bandcommunication channel is at least one item selected from a list of: apredetermined tone; a predetermined tone which is imperceptible to humanhearing; a predetermined audible pattern; a predetermined AI voice; apredetermined AI salutation; and a predetermined AI phrase.
 12. Themachine-readable medium of claim 9, wherein: the bi-directional voicecommunication session includes an in-band communication channel andout-of-band communication channel; and the predetermined indicator isrecognized from the out-of-band communication channel.
 13. Themachine-readable medium of claim 12, wherein: the predeterminedindicator recognized from the out-of-band communication channel is atleast one item selected from a list of: a predetermined calleridentification (ID) number; a predetermined caller ID string portion; apredetermined caller ID prefix; a predetermined caller ID area code; apredetermined caller ID text sequence; a predetermined text sequence;and a predetermined header portion.
 14. The machine-readable medium ofclaim 9, wherein: the connection information for the direct digitalcommunication session, is at least one of listed items of: a uniformresource locator (URL) useable to establish the direct digitalcommunication session; a token useable to authorize the direct digitalcommunication session; an identification (ID) token to identify at leastone of the AI entity and the second AI entity; and a protocol to be usedin conducting the direct digital communication session.
 15. Themachine-readable medium of claim 9, wherein: the called-number is acalled-telephone-number; and the predetermined look-up facility utilizesthe called-telephone-number and a look-up table (LUT) to determine theconnection information for the direct digital communication sessionbetween the AI entity and the second AI entity.
 16. The machine-readablemedium of claim 9, wherein: the predetermined look-up facility is atleast one of listed facilities of: a predetermined native look-upfacility maintained as a component of the AI entity; a predeterminedremote look-up facility maintained remote from the AI entity; and apredetermined centralized look-up facility accessible via apredetermined URL.
 17. A system, comprising: one or more processors; anon-transitory machine-readable storage containing instructions, whichwhen executed by the one or more processors, cause the one or moreprocessors to perform operations, the operations to: recognize, by anartificial intelligence (AI) entity on a first side of a bi-directionalvoice communication session, a predetermined indicator provided from anopposing side of the bidirectional voice communication session, thepredetermined indicator indicative of a second AI entity on the opposingside conducting the bi-directional voice communication session at acalled-number; terminate the bi-directional voice communication sessionbetween the AI entity and the second AI entity after recognition of thesecond AI entity on the opposing side; responsive to recognition of thesecond AI entity, utilize the called-number to poll a predeterminedlook-up facility to determine connection information for a directdigital communication session between the AI entity and the second AIentity; utilize the connection information to establish the directdigital communication session between the AI entity and the second AIentity; and utilize the direct digital communication session tocommunicate non-voice digital data without voice interfacefunctionality.
 18. The system of claim 17, wherein: the connectioninformation for the direct digital communication session, is at leastone of listed items of: a uniform resource locator (URL) useable toestablish the direct digital communication session; a token useable toauthorize the direct digital communication session; an identification(ID) token to identify at least one of the AI entity and the second AIentity; and a protocol to be used in conducting the direct digitalcommunication session.
 19. The system of claim 17, wherein: thecalled-number is a called-telephone-number; and the predeterminedlook-up facility utilizes the called-telephone-number and a look-uptable (LUT) to determine the connection information for the directdigital communication session between the AI entity and the second AIentity.
 20. The system of claim 17, wherein: the predetermined look-upfacility is at least one of listed facilities of: a predetermined nativelook-up facility maintained as a component of the AI entity; apredetermined remote look-up facility maintained remote from the AIentity; and a predetermined centralized look-up facility accessible viaa predetermined URL.